How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?

Erus, Güray; Loménie, Nicolas

doi:10.1016/j.patrec.2010.01.013

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…Thus, for each pair of line segment and its associated curve, one can compute the precision and reliability measures and using (5) and (6). Then, for each segmented curve, if the precision and reliability measures are not below a certain threshold given by (7) max ( , ) < 0 ,…”

Section: Precision-and Reliability-based Optimization (Pro)mentioning

confidence: 99%

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PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

Prasad

2013

Journal of Optimization

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A novel method that uses both the local and the global nature of fit for dominant point detection is proposed. Most other methods use local fit to detect dominant points. The proposed method uses simple metrics like precision (local nature of fit) and reliability (global nature of fit) as the optimization goals for detecting the dominant points. Depending on the desired level of fitting (very fine or crude), the threshold for precision and reliability can be chosen in a very simple manner. Extensive comparison of various line fitting algorithms based on metrics such as precision, reliability, figure of merit, integral square error, and dimensionality reduction is benchmarked on publicly available and widely used datasets (Caltech 101, Caltech 256, and Pascal (2007, 2008, 2009, 2010) datasets) comprising 102628 images. Such work is especially useful for segmentation, shape representation, activity recognition, and robust edge feature extraction in object detection and recognition problems.

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Section: Precision-and Reliability-based Optimization (Pro)mentioning

confidence: 99%

“…In many applications, boundaries are represented using polygonal approximation [1][2][3][4][5][6][7][8]. The problem of dominant points detection is to determine the points only from a digital curve for such representation.…”

Section: Introductionmentioning

confidence: 99%

PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

Prasad

2013

Journal of Optimization

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“…Once received the images will either be mosaicked or analyzed for super-resolution depending on the job request. This process of mosaicking [1] the images together to create a larger stitched image will require analyzing each image individually using feature recognition algorithms [2] and the satellite IMU to align the images and combine them to form one large image that spans the requested area. If the job request was to create a superresolution image the images will still need to be processed for their similar features but then the overlapping regions will have to be combined to form a higher resolution image [3] of the same region.…”

Section: Description Of Payloadmentioning

confidence: 99%

Sensor and computing resource management for a small satellite

Bhatia

Goehner

Sand

et al. 2013

2013 IEEE Aerospace Conference

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A small satellite in a low-Earth orbit (e.g., approximately a 300 to 400 km altitude) has an orbital velocity in the range of 8.5 km/s and completes an orbit approximately every 90 minutes. For a satellite with minimal attitude control, this presents a significant challenge in obtaining multiple images of a target region. Presuming an inclination in the range of 50 to 65 degrees, a limited number of opportunities to image a given target or communicate with a given ground station are available, over the course of a 24-hour period. For imaging needs (where solar illumination is required), the number of opportunities is further reduced. Given these short windows of opportunity for imaging, data transfer, and sending commands, scheduling must be optimized. In addition to the high-level scheduling performed for spacecraft operations, payload-level scheduling is also required. The mission requires that images be post-processed to maximize spatial resolution and minimize data transfer (through removing overlapping regions). The payload unit includes GPS and inertial measurement unit (IMU) hardware to aid in image alignment for the aforementioned. The payload scheduler must, thus, split its energy and computing-cycle budgets between determining an imaging sequence (required to capture the highly-overlapping data required for super-resolution and adjacent areas required for mosaicking), processing the imagery (to perform the super-resolution and mosaicking) and preparing the data for transmission (compressing it, etc.). This paper presents an approach for satellite control, scheduling and operations that allows the cameras, GPS and IMU to be used in conjunction to acquire higher-resolution imagery of a target region.

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“…In the meantime, compared with the low-and middle-resolution remote sensing images, high-resolution images can provide more detailed feature information of ground objects, such as texture size, geometric structure, and spatial layout [10]. Therefore, spatial structural features have become one of the most remarkable features of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that despite the improved performance of these previously mentioned approaches in comparison with traditional image entropy models, some issues still exist, such as parameter settings being difficult to determine, model construction being relatively complicated, and the operational performance of models needing to be further improved [37,38]. Additionally, these measurement models basically do not directly or validly reflect the information of the multilevel and multiscale features of spatial structure inherent in remotely sensed images, which greatly limits the accuracy, practicability, and application range of these methods in practical applications [10].…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

Fang

Zhou

Zhang

2019

IJGI

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Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can only reflect partial feature information at a single level (e.g., global statistical information and local spatial distribution information). The single mapping model cannot validly characterize the information of the multilevel and multiscale features of the spatial structures inherent in remotely sensed images. Additionally, the validity, practicability, and application range of the results of single-level mapping models are greatly limited in practical applications. In this paper, we present the multilevel geometrical mapping entropy (MGME) model to evaluate the information content of related attribute characteristics contained in remotely sensed images. Subsequently, experimental images with different types of objects, including reservoir area, farmland, water area (i.e., water and trees), and mountain area, were used to validate the performance of the proposed method. Experimental results show that the proposed method can not only reflect the difference in the information of images in terms of spectrum features, spatial structural features, and visual perception but also eliminates the inadequacy of a single-level mapping model. That is, the multilevel mapping strategy is feasible and valid. Additionally, the vector set of the MGME method and its standard deviation (Std) value can be used to further explore and study the spatial dependence of ground scenes and the difference in the spatial structural characteristics of different objects.

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How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?

Cited by 11 publications

References 32 publications

PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

Sensor and computing resource management for a small satellite

A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

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